Intelligent Optimization Algorithms And Their Applications

With the development of science and technology, many intelligent optimization algorithms which imitate natural phenomena or processes have been proposed to solve high-dimensional problems which have multiple local optimums. This paper studies the intelligent optimization algorithms and their applications intensively and proposes four improved algorithms to solve reliability, economic dispatch, reactive power optimization and integer programming problems. The main work of this paper is as follows:Reliability problem involves selecting the optimal combination of components and redundancy levels to meet resource constraints while maximizing system reliability.An improved particle swarm optimization (IPSO) algorithm is proposed to solve the reliability problem.The IPSO designs two position updating strategies. The IPSO introduces a mutation operator after position updating, which can prevent the IPSO from trapping into the local optimum. A large number of experiments demonstrate that IPSO algorithm has better optimization results and optimization stability than other versions of particle swarm optimization (PSO) algorithms.The economic dispatch problem (EDP) of power systems involves making generation scheduling of generators to minimize the total fuel cost while satisfying the load demand. EDP plays important role in modern power systems. This paper proposes a combination of estimation of distribution algorithm (EDA) with particle swarm optimization (PSO) algorithm to solve EDP. The algorithm is named as EDAPSO algorithm. EDAPSO combines the exploration of EDA with the exploitation of PSO. EDAPSO algorithm is used to solve the EDP of power systems with13units and40units. Experimental results show that EDAPSO can obtain better results than other algorithms when solving economic dispatch problems.The reactive power optimization problem has a significant influence on secure and economic operation of power systems. Reactive power optimization of power systems is a kind of mixed nonlinear integer programming problem and can reduce network loss by changing transformer taps, adjusting generator terminal voltage and connecting shunt capacitors. This paper proposes an improved differential evolution algorithm which is named as global differential evolution (GDE) algorithm. GDE proposes a novel mutation strategy. This strategy can improve the convergence speed and avoid local optimum. GDE algorithm is used to solve the reactive power optimization problems of IEEE14-bus, IEEE30-bus and IEEE57-bus power systems. Experimental results show that GDE algorithm is an effective algorithm to solve reactive power optimization problem.Integer programming problems are a series of complex problems. Reliability problem, task assignment problem and reactive power optimization problem are all integer programming problem. This paper proposes an effective global harmony search (EGHS) algorithm to solve integer programming problems. EGHS algorithm designs a novel improvisation method of harmony vector. This method utilizes the information of the best harmony vector in harmony memory. Experimental results show that the algorithm proposed in this paper is an effective method for solving integer programming problems.